1. Field of the Invention
The present invention relates to computerized simulation of petrophysical structure of hydrocarbon reservoirs in the earth, and in particular to determination of petrophysical models of the reservoir based on core samples from wells in the reservoir and other field data regarding the reservoir.
2. Description of the Related Art
In the oil and gas industries, the development of underground hydrocarbon reservoirs typically includes development and analysis of computer simulation models. These underground hydrocarbon reservoirs are typically complex rock formations which contain both a petroleum fluid mixture and water. One form of such reservoir model has been what is known as reservoir attributes models. For these models, the reservoir has been organized into a 3-dimensional grid of cells.
Oil and gas companies have also come to depend on geological models as an important tool to enhance the ability to exploit a petroleum reserve. Geological models of reservoirs and oil/gas fields have become increasingly large and complex. In geological models, the reservoir is also organized into a 3-dimensional grid of a number of individual cells.
Seismic data with increasing accuracy has permitted the cells in reservoir models of both types to be on the order of 25 meters areal (x and y axis) intervals. For what are known as giant reservoirs, the number of cells are the least hundreds of millions, and reservoirs of what is known as giga-cell size (a billion cells or more) are encountered.
Days or weeks of computer processing have are usually been spent on 3D model attributes computations. However, the guiding geological facies were not well digitally integrated into the model. Billions of cell attributes calculations could be wasted due to inappropriate algorithms from the initial model guide.
When characterizing and developing a reservoir field, a 3D geological model of the reservoir covering the entire 3D reservoir has been required to be built to provide an accurate model for reservoir planning Accurate indications of lithofacies are an essential input in a 3D geological modeling system. Lithofacies is based on data from wells and establishes as a basis to predict reservoir properties in cells with no well data. One of the sets of data available has been what is known are formation rock descriptions or characterizations which are made by analysts based on observations made from well core samples.
Traditionally, well core data has been assembled and analyzed based on measurements and observations taken from well core samples obtained from formation rock adjacent well boreholes at a number of depths of interest in a well. So far as is known, the core description has been hand drawn and was thereafter available for use in the form of a paper copy or at best a scanned graphical image of the hand drawing. The important geological information for a reservoir model came in a wide variety of forma and formats. This information included numerical information, text strings, graphical images, relationships and drawings. These have posed a challenge to incorporation into the reservoir model in a form that could be consistently manipulated with other data.
In addition, manual preparation of these types of well description data presented a tedious challenge in drawing standardized patterns of mineral composition, texture, sedimentary structures based on well core samples repeatedly throughout the length of the well bore. Correlating core description data among different wells which had been described by different people with different scale and size hand drawings was also often problematic. An accurate representation of the exact shape and size of formation rock features of interest in the drawing for tens of occurrences and over several wells in a reservoir was difficult to obtain in a reliable and representative manner.
The images or sketches of well core data descriptions did not lend themselves to digital interpretation and modeling applications. These images were frequently scanned as graphics image files for subsequent use, but an analyst was still provided with a visual image indicating well core data descriptions based on original analysis and formatting of such information. The images could only be displayed in the form of a visual reference picture or image.
It has been recognized that formation rock in hydrocarbon reservoirs exhibits two levels of porosity, which have been identified as macroporosity and microporosity. Oil flow characteristics in the rock are markedly different based on the relative presence of macroporosity and microporosity. The identification and relative presence of each of these levels of porosity in the reservoir rock has been based on analysis and laboratory measurement of core samples from wells in the reservoir, especially Type 1 microporosity which is charged with oil. This is described by Clerke et al. GeoArabia 2008, Vol. 13, No. 4, Application of Thomeer Hyperbolas to Decode the Pore Systems, Facies, and Reservoir Properties of the Upper Jurassic Arab D Limestone, Ghawar Field, Saudi Arabia: A “Rosetta Stone” Approach; and Clerke, SPE Journal 2009, Permeability, Relative Permeability, Microscopic Displacement Efficiency, and Pore Geometry of M_1 Bimodal Pore Systems in Arab D Limestone. The identification of the porosity level presence was linked to an understanding of the rock space architecture.
Micron level core analysis, while accurate, generally lacked the integration to the rest of the digital interpretation and modeling applications and systems. Hydrocarbons recovered from macroporosity have proven to be usually much larger than the microporosity-recovered volumes in the early years of field recovery. This has led to an imprecise and inaccurate forecast of the field ultimate recovery, i. e., the performance of the microporosity hydrocarbons has not been properly included.
Existing geological modeling processes and applications have not satisfactorily taken into account the formation rock characteristic data and the porosity level presence indicated by core samples. Specifically, proper pore system and recovery process data has, so far as is known, rarely been acquired in sufficient statistical quantities and in appropriate coordination with the geological facies.